• International Journal of Reliability and Applications
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• v.13 no.2
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• pp.81-90
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• 2012

Engineering systems are usually repairable. The reliability of a repairable system can be represented by failure intensity function. A type of shape of failure intensity function is called a failure pattern. Reliability-Centred Maintenance (RCM) presents six typical failure patterns but its definition is unclear. It is an open issue how to recognize the failure pattern of repairable systems. This paper first discusses the problems of RCM with the notion of failure pattern; then presents the method for failure pattern recognition; and finally proposes a flexible failure intensity function model. The appropriateness of the model is illustrated by a real-world example.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.301-307
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• 1998

This paper proposes free warranty interval for repairable items when the failure types of item are considered. Failure types are classified into major failure and minor failure. If major failure occurs during warranty period, the item is replaced and if minor failure occurs during warranty period, the item is minimally repaired. This paper determines the optimum free warranty interval which minimizes total expected cost of the free warranty cost model. Numerical example is shown in which failure time of item has weibull distribution.

• Journal of Applied Reliability
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• v.9 no.2
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• pp.121-130
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• 2009

The power law process model the Rate of occurrence of failures(ROCOF) with monotonic trend during the operating time. However, the power law process is inappropriate when a non-monotonic trend in the failure data is observed. In this paper we deals with the reliability modeling of the failure process of large and complex repairable system whose rate of occurrence of failures shows the non-monotonic trend. We suggest a sectional model and a change-point test based on the Schwarz information criterion(SIC) to describe the non-monotonic trend. Maximum likelihood is also suggested to estimate parameters of sectional model. The suggested methods are applied to field data from an repairable system.

• Journal of Korean Society for Quality Management
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• v.23 no.2
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• pp.43-52
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• 1995

A replacement policy under two types of failures, repairable or irrepairable, is considered, In the policy, the system is replaced at the n-th failure if all the previous (n-1) failures are repairable; Otherwise it is replaced at the first irrepairable failure. Assuming that the j-th failure is repairable with probability ${\alpha}_j$ and minimal repairs are performed for repairable failures between replacements, we derive the expected cost rate through the application of NHPP in order to determine the optimal number $n^*$. The policy includes some previous studies as special cases.

• Smart Structures and Systems
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• v.14 no.4
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• pp.559-567
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• 2014

The model of unit dynamic reliability of repairable k/n (G) system with unit strength degradation under repeated random shocks has been developed according to the stress-strength interference theory. The unit failure number is obtained based on the unit failure probability which can be computed from the unit dynamic reliability. Then, the transfer probability function of the repairable k/n (G) system is given by its Markov property. Once the transfer probability function has been obtained, the probability density matrix and the steady-state probabilities of the system can be retrieved. Finally, the dynamic reliability of the repairable k/n (G) system is obtained by solving the differential equations. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.13-19
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• 2015

Railway vehicle equipment goes back again to the state just before when failure by the repair. In repairable system, we are interested in the failure interval. As such, a statistical model of the point process, NHPP power law is often used for the reliability analysis of a repairable system. In order to derive a quantitative reliability value of repairable system, we analyze the failure data of the air brake system of the train line 7. The quantitative value is the failure intensity function that was modified, converted into a cost-rate function. Finally we studied the optimal number and optimal interval in which the costs to a minimum consumption point as cost-rate function. The minimum cost point was 194,613 (won/day) during the total life cycle of the braking system, then the optimal interval were 2,251days and the number of optimal preventive maintenance were 7 times. Additionally, we were compared to the cost of a currently fixed interval(4Y) and the optimum interval then the optimal interval is 3,853(won/day) consuming smaller. In addition, judging from the total life, "fixed interval" is smaller than 1,157 days as "optimal interval".

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.515-521
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• 2011

In this paper we consider failure detection equipment that which find failures in repairable systems and enable repair operations. In practical situations, failure detection equipment may come across troubles that can cause the omissions in detecting system failures and have a serious effect on system reliability. We analyze this effect through the appropriate modeling of Markov processes.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.2 s.25
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• pp.51-59
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• 2006

The purpose of this paper is to present reliability analysis procedures for repairable systems and apply the procedures for assessing the reliabilities of two subsystems of a specific group of military equipment based on field failure data. The mean cumulative function, M(t), the average repair rate, ARR(t), and analytic test methods are used to determine whether a failure process follows a renewal or non-renewal process. For subsystem A, the failure process turns out to follow a homogeneous Poisson process, and subsequently, its mean time between failures, availability, and the necessary number of spares are estimated. For subsystem B, the corresponding M(t) plot shows an increasing trend, indicating that its failure process follows a non-renewal process. Therefore, its M(t) is modeled as a power function of t, and a preventive maintenance policy is proposed based on the annual mean repair cost.

• Journal of Applied Reliability
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• v.7 no.1
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• pp.13-22
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• 2007

A preventive maintenance model, caller FNBM (${\alpha},{\delta},{\gamma}$) model, is proposed to decide an optimal repair number under achieved availability requirements (r) along with taking two types of failures (repairable or irrepairable) into account. In this model, the current system is replaced by a new one in case when it doesn't meet the achieved availability requirement, even though it is repairable failure; Othewise it is replaced in time of the first irrepairable failure. Assumed that the j-th failure is repairable with probability ${\alpha}_j$ minimal repairs are allowed for repairable failure between replacements. Expected cost rate for preventive maintenance model is developed using NHPP (Non - Homogeneous Poisson Process) in order to de term in the optimal number $n^*$, also numerical examples are shown in order to explain the proposed model. Since the proposed FNBM (${\alpha},{\delta},{\gamma}$) model includes Park FNBM model (1979) and Nakagawa FNBM (p) model (1983) m this proposed model is thought to be better than previous model, especially for weapon system which requires availability as primary parameter.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.859-870
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• 2011

The purpose of this paper is to develop reliability analysis procedures for repairable systems with interval failure time data and apply the procedures for assessing the storage reliability of a subsystem of a certain type of guided missile. In the procedures, the interval failure time data are converted to pseudo failure times using the uniform random generation method, mid-point method or equispaced intervals method. Then, such analytic trend tests as Laplace, Lewis-Robinson, Pair-wise Comparison Nonparametric tests are used to determine whether the failure process follows a renewal or non-renewal process. Monte Carlo simulation experiments are conducted to compare the three conversion methods in terms of the statistical performance for each trend test when the underlying process is homogeneous Poisson, renewal, or non-homogeneous Poisson. The simulation results show that the uniform random generation method is best among the three. These results are applied to actual field data collected for a subsystem of a certain type of guided missile to identify its failure process and to estimate its mean time to failure and annual mean repair cost.